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decisions related to turbine inspection and maintenance with a human expert [4, 5]. This can be based on a deep reinforcement learning framework, which interactively optimises key performance indicators in
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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simulation skills while gaining deep expertise in electromagnetic propagation, sensor technology, and applied physics. Why Cranfield? Cranfield University is a recognised leader in defence, aerospace, and
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about our proposed methodology. In this method, the images taken by each drone will be loaded into the pre-processing unit and then the pre-processed data will be used as the input of the deep learning
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the Infrared sensors are working in overlapping field of view The Person (Essential) Knowledge, Skills and Experience Track record of design, deployment and evaluation of machine/deep learning-based techniques
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. ML-Based Predictive Models: Deep learning and surrogate modeling techniques will be employed to predict structural response under varying loads and detect early signs of fatigue or failure. 3. Real
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the challenge of time-consuming sideshaft testing. As a key member of the team, you will apply cutting-edge machine learning and deep learning techniques to dramatically reduce testing cycles. You will lead life
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to achieve, at least a 2.1 honours degree or a master’s in a relevant science or engineering related discipline. Applicants should have strong background in Machine Learning and Deep Learning. To apply, please
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applications. What you will working on: Building an open-world deep learning framework for human activity analysis. Implementing continual learning to seamlessly integrate new knowledge. Validating results
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: Dr. Xiatian Zhu (University of Surrey) – Expert in computer vision, generative AI, and multimodal learning. Dr. Xun Xu(A*STAR, Singapore) – Researcher in data and resource efficient deep learning